Eruditions Publishing
Introduction to Neural Networks
and Data Mining
for Business Applications
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Aimed at business students, not at students of
electrical engineering as most other books in the field
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Techniques are applied to solving business problems
throughout the text
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Includes case studies and examples from retail,
marketing, insurance, telecommunications, banking and finance, and operations
management
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Provides a history of the development of neural
networks and their impact on business
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Discusses the Perceptron neural model and its
limitations
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Includes backpropagation, the most commonly
used learning paradigm for business applications of neural networks
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Includes case studies showing backpropagation
learning applied to classification and prediction problems
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Shows how self-organisation is particularly
suited to clustering of large data sets
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Covers adaptive resonance theory
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Shows backpropagation and self-organisation in
the context of data mining
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The purposes of data mining are outlined
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A methodology for mining hidden information from
large data sets is presented
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Covers the concepts of directed and undirected
knowledge discovery
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Includes data mining case studies from the insurance
and telecommunications industries
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Introduces other intelligent techniques like genetic
algorithms, fuzzy logic, expert systems, and hybridisations of these techniques
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Speculates about the future of neural network
research and business applications
Contents
(scroll down -->)
Chapter 1
Introduction
1.1 Overview of Neural Networks in Business
1.2 Principles of Neural Networks
1.3 History of Neural Networks
1.4 Overview of Application Areas
Chapter Summary
Chapter 2
Perceptrons
2.1 Early Models
2.2 Classification
2.3 Perceptron Learning Algorithms
2.4 Limitations of Perceptrons
Chapter Summary
Chapter 3
Multilayered Networks
3.1 Architecture
3.2 Credit Assignment Problem
3.3 Multilayered Feedforward Neural Networks
3.4 Backpropagation Learning Rule
3.5 Training & Implementation Issues
Chapter Summary
Chapter 4
Case Studies
4.1 Classification: Loan Applicant Evaluation
4.2 Prediction: Foreign Exchange Rate Prediction
Chapter Summary
Chapter 5
Self-Organisation
5.1 Principles of Self-Organisation
5.2 Clustering
5.3 Unsupervised and CompetitiveNeural Networks
5.4 Self-Organising Feature Maps
5.5 Adaptive Self-Organisation
5.6 Applications of Self-Organisation
Chapter Summary
Chapter 6
Data Mining
6.1 Overview
6.2 Data Mining Methodologies
6.3 Knowledge Discovery
6.4 Commercial Data Mining
Chapter Summary
Chapter 7
Data Mining Case Studies
7.1 Data Mining in the Insurance Industry
7.2 Data Mining in the Telecommunications
Industry
Chapter Summary
Chapter 8
Other Intelligent Techniques
8.1 Genetic Algorithms
8.2 Fuzzy Logic
8.3 Expert Systems
8.4 Hybridisation of Intelligent Techniques
Chapter Summary
Chapter 9
Conclusion
9.1 Current Research
9.2 Future Research
9.3 Business Applications versus Research
9.4 Final Remarks
Bibliography
Appendix A Viewing
Instructions for Magic Eye 3D Image
Appendix B Review of Mathematics
Appendix C Questions
for Credit Application Example
Index
160pp Papercover 245mm x 175mm
ISBN 1-86491-004-6 RRP AU$54.95 (Incl. GST)
Published: 1999
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